A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.
Objectives: In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesion...
| Publicado en: | European Radiology Vol. 23; no. 8; pp. 2051 - 2061 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104080256&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104080256 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2013 vid: 23 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104080256 NLM23579418 2012177799 10.1007/s00330-013-2804-3 NLM23579418 104080256 ppf: 2051 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. aug: au: Baltzer, Pascal A T Dietzel, Matthias Kaiser, Werner A affil: Department of Radiology, Medical University Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria, pascal.baltzer@meduniwien.ac.at. sug: subj: Breast Neoplasms Classification Breast Neoplasms Diagnosis Decision Trees Magnetic Resonance Imaging Methods Mammography Methods Adult Aged Algorithms Breast Neoplasms Pathology Contrast Media Diagnostic Use Diagnosis, Differential Female Human Image Processing, Computer Assisted Methods Middle Age Multivariate Analysis Probability ROC Curve Reproducibility of Results Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female ab: Objectives: In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM.Methods: A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree.Results: A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %.Conclusions: The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %.Key Points: • A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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